Data Analysis Device Simplifies Decision Tree Rules
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Solution Overview
Problem
Existing data analysis techniques, such as decision trees, become overly complex when dealing with systems that generate data with various attributes, and fail to accurately classify states or outputs due to the limitation of considering only single rules, overlooking multiple important rules that could improve classification accuracy.
Innovation Solution
A data analysis device that generates nodes based on conditions related to explanatory variables, evaluates the target values, and extracts parameters to visualize classification rules, allowing for the simplification of decision trees and consideration of multiple attribute combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a decision tree is constructed to classify data with various attributes, then classification accuracy is improved, but the decision tree becomes too complicated
Solution Approach 1:
The patent segments the complex decision tree into multiple simple classification rules. Instead of constructing one comprehensive decision tree that becomes complicated, the system divides the classification task into multiple independent rules, each handling specific attribute combinations. This segmentation maintains classification accuracy while avoiding the complexity of a single large decision tree.
Solution Approach 2:
The patent extracts important classification rules from the decision tree structure and presents them as separate, standalone rules. By taking out the essential classification logic from the complex tree structure and representing it as simplified rules with importance scores, the system maintains accuracy while reducing complexity for user interpretation.
2Ease of operation
If the decision tree is trimmed to simplify it, then ease of operation is improved, but classification accuracy may be reduced
Solution Approach 1:
The patent changes the representation parameter from a trimmed decision tree structure to a set of classification rules with importance scores. Instead of simplifying the decision tree by removing nodes (which loses information), the system transforms the output format to show multiple rules ranked by importance, maintaining accuracy while improving interpretability through the importance scoring mechanism.
3Device complexity
If only one classification rule is considered, then device complexity is reduced, but multiple important rules are overlooked
Solution Approach 1:
The patent implements a dynamic approach by generating multiple classification rules with varying importance scores rather than selecting a single static rule. The system adaptively determines which rules are most important based on the data characteristics and presents them in order of importance, allowing users to consider multiple relevant rules without being overwhelmed by all possible rules.
Data Source
AI summary
A data analysis device that analyzes data having a record including an objective variable and a plurality of explanatory variables includes a node generating unit that generates a node specified by a condition of the explanatory variable on the basis of the objective variable and the explanatory variable of the record and associating the record with the node, an evaluation value generating unit that generates a proportion of the number of records whose target value is the objective variable among a plurality of records associated with the node as an evaluation value, and a parameter extracting unit that selects a node on the basis of the evaluation value and extracts and outputs the condition of the explanatory variable related to the selected node.